The first Lidar-only odometry framework with high performance based on truncated least squares and Open3D point cloud library, The foremost improvement include:

• Fast and precision pretreatment module, multi-region ground extraction and dynamic curved-voxel clustering perform ground point extraction and category segmentation.
• Feature extraction based on principal component analysis(PCA) elaborate four distinctive feature，including: planar features, ground features, edge features, sphere features
• There are three kinds of residual functions based on truncated least squares method for directly processing above features which are point-to-point, point-to-line, and point-to-plane.
• Open3d point cloud library is integrated into SLAM algorithm framework for the first time. We extend more functions and implemented the message interface related to ROS.

Note that regard to pure odometry without corrections through loop closures, T-LOAM delivers much less drift than F-LOAM.

Each frame of the 3D LiDAR is processed as input. Four main processing modules are introduced to construct the backbone of the algorithm: (a) multi-region ground extraction module, (b) dynamic curved-voxel clustering module, (c) feature extraction module, (d) pose optimization module.

-ROS(Melodic Ubuntu18.04)

sudo apt-get install python-catkin-tools ros-melodic-ecl-threads ros-melodic-jsk-recognition-msgs ros-melodic-jsk-visualization ros-melodic-velodyne-msgs

-YAML(0.6.3) Note that you must build a shared library due to we utilize the ros nodelet package.

tar -zxvf yaml-cpp-yaml-cpp-0.6.3.tar.gz
cd yaml-2.3.0 && mkdir build && cd build
cmake [-G generator] [-DYAML_BUILD_SHARED_LIBS=ON] ..
make 
sudo make install

-Open3D(A Modern Library for 3D Data Processing 0.12.0)

Please note that open3d installation will be a slightly troublesome process, please be patient. Another problem that needs attention is that Open3D-ML cannot be used in ROS at the same time due to the link error2286 and error3432. In order to fix this, you need to specify the cmake flag -DGLIBCXX_USE_CXX11_ABI=ON. However, the latest Tensorflow2.4 installed through conda(not pip) already supports the C++11 API, you can check the API with print(tensorflow.__cxx11_abi_flag__). If the flag is true, you can set the compile flag -DBUILD_TENSORFLOW_OPS=ON Next, you can complete the installation according to the instructions

cd Open3D
util/scripts/install-deps-ubuntu.sh
mkdir build && cd build 
cmake \
    -DBUILD_SHARED_LIBS=ON \
    -DPYTHON_EXECUTABLE=$(which python3) \
    -DBUILD_CUDA_MODULE=ON \
    -DGLIBCXX_USE_CXX11_ABI=ON \
    -DBUILD_LIBREALSENSE=ON  \
    -DCMAKE_BUILD_TYPE=Release \
    -DCMAKE_INSTALL_PREFIX=/usr/local \
    -DBUILD_PYTORCH_OPS=OFF \
    -DBUILD_TENSORFLOW_OPS=OFF \
    -DBUNDLE_OPEN3D_ML=ON \
    -DOPEN3D_ML_ROOT=${replace with own Open3D-ML path} \
    ../
make -j4
sudo make install 

If you have clone problems, you can download it directly from the link below.

Baidu Disk code: khy9 or Google Drive

-Ceres Solver(A large scale non-linear optimization library 2.0) you can complete the installation according to the guide

Now create the Catkin Environment:

mkdir -p ~/tloam_ws/src
cd ~/tloam_ws
catkin init
catkin config --merge-devel
catkin config --cmake-args -DCMAKE_BUILD_TYPE=Release

And clone the project:

cd src
git clone https://github.com/zpw6106/tloam.git
catkin build

Download the KITTI Odometry Dataset (Graviti can provide faster download speed in China), then organize it according to the following structure, and modify the read path in the config/kitti/kitti_reader.yaml

-Example for running T-LOAM using the KITTI Dataset

roslaunch tloam tloam_kitti.launch

Pengwei Zhou (Email: zpw6106@gmail.com)

Thank you for citing our T-LOAM paper on IEEEif you use any of this code:

@ARTICLE{9446309,
  author={Zhou, Pengwei and Guo, Xuexun and Pei, Xiaofei and Chen, Ci},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={T-LOAM: Truncated Least Squares LiDAR-Only Odometry and Mapping in Real Time}, 
  year={2021},
  volume={},
  number={},
  pages={1-13},
  doi={10.1109/TGRS.2021.3083606}
  }

We hereby recommend reading A-LOAM ,floam and TEASER for reference and thank them for making their work public.

The source code is released under GPLv3 license.

I am constantly working on improving this code. For any technical issues or commercial use, please contact me(zpw6106@gmail.com).